I was recently asked to give a short interview at Vetenskapsradion (“Science radio”) in Sweden on the research area of forecasting, its usefulness to society and industry, and the recent International Symposium of Forecasting 2021! Thanks for the invitation and the opportunity to discuss about the research area!
You can find a short summary and a sound bite (in swedish) here!
We have a great team interested in multiple areas of Artificial Intelligence, data science, and predictive modelling. You can find some more about our team here.
Having relatively recently moved to the University of Skövde myself, and in Sweden more generally, my personal view is that it is a great place to work, with a strong collegial environment, that not only respects academic freedom, but also strives for a healthy work-life balance. I have also found very refreshing the pro-innovation culture in industry and academia.
I should add, for the non-swedes, that the university is fairly international, with discussions, research, and teaching happening all in English. Some swedish is always useful, but not critical. Nonetheless, it is a fun language to learn!
I am looking forward to see our team expand and embark to new exciting research and projects!
Beyond the contacts offered in the job posts, feel free to reach out to me as well.
Kandrika F. Pritularga, Ivan Svetunkov, and Nikolaos Kourentzes, 2021. International Journal of Production Economics.
Hierarchical forecasting has been receiving increasing attention in the literature. The notion of coherency is central to this, which implies that the hierarchical time series follows some linear aggregation constraints. This notion, however, does not take several modelling uncertainties into account. We propose to redefine coherency as stochastic. This allows to accommodate overlooked uncertainties in forecast reconciliation. We show analytically that there are two potential sources of uncertainty in forecast reconciliation. We use simulated data to demonstrate how these uncertainties propagate to the covariance matrix estimation, introducing uncertainty in the reconciliation weights matrix. This then increases the uncertainty of the reconciled forecasts. We apply our understanding to modelling accident and emergency admissions in a UK hospital. Our analysis confirms the insights from stochastic coherency in forecast reconciliation. It shows that we gain accuracy improvement from forecast reconciliation, on average, at the cost of the variability of the forecast error distribution. Users may opt to prefer less volatile error distributions to assist decision making.
Nikolaos Kourentzes, Andrea Saayman, Philippe Jean-Pierre, Davide Provenzano, Mondher Sahli, Neelu Seetaram, and Serena Volo, 2021, Annals of Tourism Research 88: 103197.
COVID-19 disrupted international tourism worldwide, subsequently presenting forecasters with a challenging conundrum. In this competition, we predict international arrivals for 20 destinations in two phases: (i) Ex post forecasts pre-COVID; (ii) Ex ante forecasts during and after the pandemic up to end 2021. Our results show that univariate combined with cross-sectional hierarchical forecasting techniques (THieF-ETS) outperform multivariate models pre-COVID. Scenarios were developed based on judgemental adjustment of the THieF-ETS baseline forecasts. Analysts provided a regional view on the most likely path to normal, based on country-specific regulations, macroeconomic conditions, seasonal factors and vaccine development. Results show an average recovery of 58% compared to 2019 tourist arrivals in the 20 destinations under the medium scenario; severe, it is 34% and mild, 80%.
The PhD Student Council at the University of Skövde hosted a panel discussion on the topic of AI in research. Together with Peter Anderberg, Jörgen Hansson, Amos Ng, and Jane Synnergren, we discussed our views, informed by the perspectives from our different areas of research. We touched upon various aspects of the topic, and concluded with questions from the audience.
Many thanks to Sara Yousif Mohamed Mahmoud and Vipul Vijayan Nair for the invitation and organising a very enjoyable and interesting event!
You can watch the recording at Sara’s youtube channel.
The slides for my presentation at the International Symposium on Forecasting 2020 are available here.
Building on the work by Pangiotelis et al. (2020) we investigate the implications of the geometric interpretation of hierarchical forecasting further. We propose a new framework for generating hierarchical forecasts, which encompasses previous hierarchical methods while providing insights on their behaviour. A key takeaway is that it is possible to obtain more efficient solutions to the hierarchical forecasting problem. Nonetheless, even though these may be more efficient, the optimisation remains non-trivial. In this work, we identify a series of approximations that balance the efficiency gains with the optimisation complexity to provide superior hierarchical forecasts, as evidenced in our empirical evaluation.
We are inviting submissions to a special issue at the International Journal of Forecasting on the topic of “Innovations in Hierarchical Forecasting”. The special issue is guest edited by G. Athanasopoulos, R. J. Hyndman, A. Panagiotelis and myself and its submission deadline is on the 31st of August 2021.
Organisations make multiple decisions informed by forecasts both to plan and function efficiently. Such decisions may differ in scope, from operational, to tactical, to strategic; corresponding to different time scales from short-term to medium-term to long-term; and can have different foci, for example inventory control for a single product, for a single store, or across an entire supply chain. Organisations that face such challenges include businesses, not-for-profit organisations, and policymakers who address societal challenges.
Forecasts of quantities that adhere to some known constraints should be coherent; that is, the predicted values at disaggregate scales should add up to the aggregate forecast. For example, monthly predictions should sum up to annual predictions and similarly, regional predictions should add up to country-level predictions. This is an important qualifier for forecasts to support aligned decision making across different planning units and horizons.
This forecasting problem setting gives rise to hierarchical forecasting. Historically this has been addressed using Top-Down and Bottom-Up approaches, which have been shown to exhibit several limitations. In the past decade, the introduction of forecast reconciliation approaches has reinvigorated research into hierarchical forecasting. Recent work has looked at novel estimation techniques, expansion of the hierarchical framework to temporal and cross-temporal hierarchies, probabilistic hierarchical forecasting, alternative understandings of the problem through a geometric or optimisation lens, amongst many other contributions. Meanwhile, forecast reconciliation techniques have been applied to a number of novel domains including energy, macroeconomics, mortality, tourism, and intermittent demand.
Areas of interest include, but are not limited to:
New methodologies for hierarchical forecasting
High dimensional hierarchical forecasting (methods and applications)
An improved understanding of the relationship between forecast reconciliation and forecast combination
Probabilistic hierarchical forecasting
Temporal and cross-temporal hierarchies
Machine learning and AI approaches to hierarchical forecasting
Hierarchical forecasting with explanatory variables
Applications of hierarchical forecasting to new domains
We would like to see submissions from diverse backgrounds, reflecting the nature of forecasting in organisations. You can find additional information here.
Together with Devon Barrow and Sven Crone, we gave a talk at the recent OR 62 conference, moderated by Christina Phillips. The topic was: “The quest for greater forecasting accuracy: Perspectives from Statistics & Machine Learning”. I have worked with both Devon and Sven in the past years and the three of us share quite a few perspectives on what are the promising avenues for forecasting, but also have our diverging views, influenced by our research interests and interactions with the industry. The discussion reflects that, and I think that there are a few helpful points about the future of the various disciplines in forecasting. Of course, we dutifully avoid making too many forecasts about forecasting!
Last week we run the first workshop of the Forecasting Forum Scandinavia, hoping to start an ongoing discussion between academia and practice around forecasting and predictive analytics. The vision is for this to be the catalyst in:
providing innovative solutions to real business problems, at a rigorous scientific standard;
shorten the path to implementing innovative and impactful research to practice;
create consortia between and within industry and academia to facilitate ambitious research by sharing know-how, resources, and risk.
The topic of the workshop the use of information from the business and market environment to enhance forecasts. You can find slides and recordings of all the talks in the forum’s LinkedIn group.
My talk focused on the academic perspective, and a gave a non-technical overview of:
What are the elements of a “good” forecast? (I keep the quotation, as I did not touch upon loss functions and objectives.)
Limitations of extrapolative forecasting and some motivations for using external predictors. (You won’t get me saying causal models! We are still so far away from being able to claim causality!)
Potential variables to enhance your forecasts and relevant considerations.
You can find the slides here and a recording of the talk below.
A few words on the “we”. With David Fagersand, who is the CEO of Indicio Technologies, we share the view that there is a substantial gap in the interaction between academia and industry on forecasting and predictive analytics, at least in Sweden and neighbouring Scandinavia – although my experience is that this is a wider challenge (more on that below!). We both recognise that there is strength in putting different perspectives and objectives together, to keep some balance between academia and practice. I do not think it is contested that academia can be “too academic” at times, and practice “too practical” (see a previous opinion piece co-authored with Fotios Petropoulos here). Obviously, Indicio is a company and therefore for-profit. I nowadays work at a Skövde university in Sweden, that is a public university, which in line with my ethos for freely accessible knowledge and open-source. My personal view is that bringing these two sides together can only be beneficial! My view for the ideal evolution of initiative is to be less driven by individuals, and more by the interaction in the community. It would be great if organisations would openly speak about the challenges they face and provide the means to universities to help them solve them. It would also be great if more academics would get their hands dirty! And obviously outstanding if it is widely acknowledged that such an initiative to run requires both resources and speakers! So, a call for action for current and future members!
I will expand a bit on this. Over my academic career I had the luck to work with many great colleagues and some of the biggest companies internationally. Naturally, at every country the business culture and the academic attitude differs. It will come as no surprise than some foster impactful research and innovation more than others. I find Sweden to be a great place for this, with both companies and universities focusing more into how to get exciting work done, rather than how to split the pie – necessary, but let’s get the priorities right when you involve academia: we are not consultants (at that point!). I find that organisations (and that includes universities ironically!) often do not understand how resource intensive research has become. It needs time, very skilled people, computing resources, data and time. Did I mention time? More importantly, training new academics is critical, and that requires the investment by all industry, academia and state. Let me also add that the skill often does not come solely from academia. We are all smart people, so if we ask someone/a team to outsmart us all and solve a very difficult problem, at least let’s give then the resources!
I would not expect from academics to always get the economics right (unless they are economists? – of course we have the responsibility to get it right!), but if companies are into money making, they surely understand that there is no free lunch! We face great societal challenges, and we all need to play our part. Improving forecasts is not just fun (for academics), or impacting the bottom line (for companies), it is also important for a more sustainable society and environment in the large scheme of things, but also for meeting the needs of societies. The initiative is called Forecasting Forum Scandinavia, but I would so much like to see the name proving to be wrong and becoming an international community of people eager to solve problems, meet challenges, and contribute!
I am delighted to receive the news that my recent paper with George Athanasopoulos at the European Journal of Operational Research has been selected as the EJOR editor’s choice article for June 2020. My thanks to the editor and the reviewers for their help with their comments and recommendations in improving the paper and bringing it to its current form.
You can find the June 2020 selected papers here. The final online version of article will be available for free for the next three months.
I plan to write a post about the gist of the idea.